Today, minimally invasive intravascular therapies are routinely performed in catheterization laboratories to treat numerous diseases. These diseases include strokes, vessel malformations, stenoses and tumors. Minimally invasive intravascular therapies are typically performed under image guidance in order to give the physician or health professional valuable real-time feedback, for example, visualization and localization of catheters and other tools/objects relative to the anatomy of interest of a patient. Various imaging systems and techniques may be used to provide this image guidance. For example, during these interventions, angiographic imaging systems may acquire data such as fluoroscopy sequences, 3D datasets (C-arm CT imaging), and 2D DSA image sequences.
DSA is an imaging method in which radiographic images of blood vessels are produced by subtracting a pre-contrast image (i.e., the “mask image”) from later images after a contrast agent has been administered to the patient. Background anatomical structures such as bones and soft tissue are removed, leaving the blood vessels clearly visualized and highly contrasted against a neutral background. DSA has long been a preferred technique for visualizing blood vessels in medical procedures, when appropriate.
Using image post-processing algorithms and techniques, DSA sequences may also provide significant quantitative data concerning changes in blood flow and, consequently, an evaluation of a respective intravascular therapy. For example, time-density (or time-contrast) curve analysis can track changes of contrast agent density as a function of time in the DSA sequences. Generally, time-contrast curves are obtained or generated from a DSA image sequence by selecting a same region of interest in the DSA input images (also referred to as “frames”) and measuring the average contrast density value within the region for each frame of the DSA sequence corresponding to a particular time (t). The area-average contrast density values are then plotted as a function of time. FIG. 1 shows a typical time-contrast curve. The start of the curve corresponds to the contrast inflow to the region of interest, with the upward curve slope indicating the rate of contrast inflow. The peak of the curve directly correlates to the amount of contrast agent in the region of interest. The back of the curve relates to the time the contrast remains in the region of interest.
Recently introduced, the syngo iFlow software product enables users a novel view of DSA data by condensing all the information inside a complete DSA sequence into one single color image using advanced color coding techniques. In essence, every pixel of the single, generated image encodes time information describing the passing-through of the contrast agent, e.g. at the time point of maximal opacification, i.e., the “time-to-peak opacification” (which is the information actually being encoded in the current version of the iFlow product). Alternatively, every pixel could contain information about the time point of the highest slope of the corresponding time-contrast curve, or any other temporal parameter determined from the DSA sequence and color encoded accordingly. This color-coding approach is described further in an article by C. J. Lin, S. C. Hung, W. Y. Guo, F. C. Chang, C. B. Luo, J. Beilner, M. Kowarschik, W. F. Chu, and C. Y. Chang, entitled “Monitoring Peri-Therapeutic Cerebral Circulation Time: A Feasibility Study Using Color-Coded Quantitative DSA in Patients with Steno-Occlusive Arterial Disease”, American Journal of Neuroradiology, Epub April 2012, 6 pages, and color-coding of DSA sequences is more generally described in an article by C. M. Strother, F. Bender, Y. Deuerling-Zheng, K. Royalty, K. A. Pulfer, J. Baumgart, M. Zellerhoff, B. Aagaard-Kienitz, D. B. Niemann, and M. L. Lindstrom, entitled “Parametric Color Coding of Digital Subtraction Angiography”, American Journal of Neuroradiology, May 2010, pp. 919-924, Vol. 31, No. 5, each of the above references being hereby incorporated by reference herein.
The following outlines the existing color coding approach. The input image data for the syngo iFlow algorithm is a time series of 2D acquisitions, where every pixel in each input image/frame of the DSA image sequence represents a path measurement of the object's x-ray attenuation along the x-ray from the x-ray source to the corresponding detector pixel. For each image pixel, this allows for the calculation of Imax (representing the maximum of the amount of contrast agent along the respective x-ray) and Tmax (representing the time point of the maximum opacification due to contrast agent) as follows:
                    I                  ma          ⁢                                          ⁢          x                    ⁡              (                  x          ,          y                )              =                  max                              t            a                    ≤          t          ≤                      t            b                              ⁢                        [                                    I              ⁡                              (                                                      t                    mask                                    ,                  x                  ,                  y                                )                                      -                          I              ⁡                              (                                  t                  ,                  x                  ,                  y                                )                                              ]                ⁢                                  ⁢        and                                          T                      ma            ⁢                                                  ⁢            x                          ⁡                  (                      x            ,            y                    )                    =                        max_at                                    t              a                        ≤            t            ≤                          t              b                                      ⁡                  [                                    I              ⁡                              (                                                      t                                          ma                      ⁢                                                                                          ⁢                      sk                                                        ,                  x                  ,                  y                                )                                      -                          I              ⁡                              (                                  t                  ,                  x                  ,                  y                                )                                              ]                      ,  where the image sequence is defined as I(t, x, y); the time point that defines the mask image is tmask; and the start and end time points for the iFlow image are defined as ta and tb, respectively. Note that the mask image in DSA imaging is needed for subtraction purposes in order to get rid of all anatomical background.
As a last step, the syngo iFlow algorithm uses color coding to fit the information into a single pixel. For each image pixel, the Tmax value is encoded by assigning a pure red color value to t=0 (a user-specified starting time point), a pure blue color value to a user-specified end time point, a pure green color value to the half-time of this interval and linearly interpolating the value between the three colors. The Imax value is normalized to [0 . . . 1] (i.e., all the Imax values lay in the range 0 to 1) and used as the opacity for the pixel. Thus, the time of the maximum opacification of each pixel becomes associated with a color and the various colors represent the early, middle and late flow in the section of the DSA sequence being viewed. The colors do not relate necessarily to the typical phases of contrast flow. The result is a single “composite time” image showing the history of the contrast agent flow. FIG. 3 illustrates this color look-up, i.e., the iFlow color encoding for one pixel, with reference to a time-contrast curve. FIG. 3 shows the single, resulting image after applying the color look-up for each pixel of FIG. 2 (which shows a red through blue spectrum bar along the time axis, the marked Tmax being in the light green scale). In FIG. 3, the brightest blood vessels (mainly in the image center, from bottom to middle) are color-coded in red, orange or yellow, the medium bright blood vessels (the smaller vessels throughout the image but mainly surrounding the center) are color coded in light green, green, blue-green or light blue, and the darkest blood vessels (along the left and bottom left periphery of the image) are color coded blue or dark blue.
Improvements to this existing color coding method are possible. Since the existing method visualizes all the temporal information inside one color image, it requires the user to visually scan this image thoroughly for the information they need. Details can therefore be overlooked easily. Also, contrary to the static nature of the iFlow images, the human visual system is optimized for detecting motion. A static image does not take advantage of this and a visualization method that allows for animating the information of iFlow images could enhance the existing method.